HIL Development and Validation of Lithium-Ion Battery Packs

Lee, SoDuk; Cherry, Jeff; Lee, Byung-Ho; McDonald, Joseph; Safoutin, Michael

doi:10.4271/2014-01-1863

Cited by 13 publications

(9 citation statements)

References 8 publications

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“…Even though Yu [14][15][16][17][18] as coauthor is the main contributor in the area. He accounts for five articles, followed by Li [19][20][21][22], Mohammed [23][24][25][26], Wong [27][28][29][30], Yap [27][28][29][30], and Yaw [27 -30] with four articles each one. Figure 5 presents the coauthor collaboration network from articles and chapter book published during the 2017-2022 period time, on multi-agent systems and machine learning applications, based on full counting.…”

Section: Step 3 Develop the Argumentmentioning

confidence: 99%

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Multi-Agent Technologies and Machine Learning

2023

Artificial Intelligence

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is a full member of the Russian Academy of Sciences (RAS) and a scientific supervisor and chair of the Science-Technological Board of the National Computer Corporation (NCC), Russia. His main areas of research are systems analysis, artificial intelligence, data science, and digital economy. He is a co-chair of the Advanced Mathematical Tools for Data-Driven Applied Systems Analysis task group established by the Committee on Data (CODATA) of the International Science Council (ISC), a vice-chair of the Committee for Systems Analysis of the RAS, and a vice-chair of the RAS Scientific Board for Robotics and Mechatronics. During 2014-2020 he was a member of the Science Advisory Committee (SAC) of the International Institute for Applied Systems Analysis (IIASA). During 2020-2022 he was a chair of the joint CODATA-IIASA working group Big Data and Systems Analysis.

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Section: Step 3 Develop the Argumentmentioning

confidence: 99%

“…Value decomposition network (VDN) [23] is the first DMARL method that addresses the credit assignment problem. VDN represents joint action value as a summation of local (individual agents') action values conditioned on agents' local observations.…”

Section: Deep Marlmentioning

confidence: 99%

Multi-Agent Technologies and Machine Learning

2023

Artificial Intelligence

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“…There are simple TMS designs which use the standard vapour compression cycle and positive temperature coefficient heater (PTC) to serve the cabin and a simple radiator coolant loop to maintain inverter and electric motor temperature [18,19]. For the battery, these systems use the battery management system (BMS) to adjust power limits, reducing heat generation, which leads to battery overheating [20]. To combat lithium-ion battery ageing, degradation, and thermal runaway, it has become a common practice to include a third loop responsible for controlling lithiumion battery temperature to a 15 • C to 35 • C range [21].…”

Section: Introductionmentioning

confidence: 99%

Octovalve Thermal Management Control for Electric Vehicle

Wray

Ebrahimi

2022

Energies

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In the pursuit of more efficient vehicles on the world’s roads, the vehicle thermal management system has become a limiting factor when it comes to EV range and battery life. In extreme climates, if the thermal system cannot pull down or warm up the EV powertrain in a timely manner, the battery is at serious risk of capacity loss or accelerated degradation. As waste heat is inherently limited with EVs, the way in which we provide the heat for warm-up must be as efficient as possible to reduce the load on the battery. In this paper, a revolutionary waste heat recovery (WHR) thermal management system designed by Tesla, nicknamed the ‘Octovalve’, is described, modelled, and simulated. This paper contributes to collective knowledge by presenting an in-depth breakdown of the key operating modes and outlining the potential benefits. Modelled in the multidomain Simulink Simscape software, the octovalve’s performance is directly compared to a typical EV WHR thermal management system. The system under analysis is shown to significantly reduce EV energy consumption and battery load during warm-up but at the cost of overall warm-up time. Unlike any other WHR system found in literature, this system has a heat pump with can perform air conditioning and heat pump tasks simultaneously, which is shown to have a remarkable impact on energy efficiency and battery life.

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“…A lithium-ion (Li-ion) battery pack model was used for simulation of both the P0 and P2 MHEVs. Te battery pack model [6,7,8] contains a two-time constant equivalent circuit battery cell model, a lumped capacitance battery thermal model and battery management system (BMS) thermal control strategies. Initial model development and validation reported in this paper were conducted using GT-DRIVE, but the DLLs and modeling strategies have been ported into ALPHA for use in future EPA MHEV analyses.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Controls Development of 48 V Mild Hybrid Electric Vehicles

Lee¹,

Cherry²,

Safoutin³

et al. 2018

SAE Technical Paper Series

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T he Advanced Light-Duty Powertrain and Hybrid Analysis tool (ALPHA) was created by EPA to evaluate the Greenhouse Gas (GHG) emissions of Light-Duty (LD) vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulator capable of analyzing various vehicle types combined with diferent powertrain technologies. Te ALPHA desktop application was developed using MATLAB/Simulink. Te ALPHA tool was used to evaluate technology efectiveness and of-cycle technologies such as air-conditioning, electrical load reduction technology and road load reduction technologies of conventional, non-hybrid vehicles for the Midterm Evaluation of the 2017-2025 LD GHG rule by the U.S. Environmental Protection Agency (EPA) Ofce of Transportation and Air Quality (OTAQ). Tis paper presents controls development, modeling results, and model validation for simulations of a vehicle with a 48 V Belt Integrated Starter Generator (BISG) mild hybrid electric vehicle and an initial model design for a 48 V inline on-axis P2-confguration mild hybrid electric vehicle. Both confgurations were modeled with a MATLAB/Simulink/Statefow tool, which has been integrated into EPA's ALPHA vehicle model and was also used to model components within Gamma Technology GT-DRIVE simulations. Te mild hybrid electric vehicle model was validated using vehicle data obtained from Argonne National Laboratory (ANL) chassis dynamometer tests of a 2013 Chevrolet Malibu Eco 115 V 15 kW BISG mild hybrid electric vehicle. Te simulated fuel economy, engine torque/speed, motor torque/speed, engine on-of controls, battery voltage, current, and State of Charge (SOC) were all in good agreement with the vehicle test data on a number of drive schedules. Te developed 48 V mild hybrid electric vehicle model can be used to estimate the GHG emissions and fuel economy of 48 V mild hybrid electric vehicles over the EPA regulatory drive cycles and to estimate of-cycle GHG emissions. Te 48 V mild hybrid electric vehicle model will be further validated with additional 48 V mild hybrid electric vehicle test data in the future as more vehicle models become available. EPA has included 48 V BISG mild hybrid electric vehicle technology in its assessment of CO2-reducing technologies available for compliance with U.S. GHG standards.

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HIL Development and Validation of Lithium-Ion Battery Packs

Cited by 13 publications

References 8 publications

Multi-Agent Technologies and Machine Learning

Multi-Agent Technologies and Machine Learning

Octovalve Thermal Management Control for Electric Vehicle

Modeling and Controls Development of 48 V Mild Hybrid Electric Vehicles

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